AI Content Detector.

Early AI detectors were trained almost exclusively on GPT-3 and GPT-3.5 outputs. As the AI model landscape diversified, these detectors became less reliable on text from newer models (GPT-4o, Claude, Gemini) and essentially ineffective on models outside their training distribution.

The detection challenge in 2026 is multi-model. A content team reviewing freelance submissions does not know which AI tool the writer used. An educator checking student work faces outputs potentially from any model. A publisher screening articles needs to handle whatever the contributor used.

Multi-model detection requires training data from all major models. The current version of this tool was trained on labeled outputs from GPT-4o, GPT-3.5, Claude 4 (Sonnet, Opus, Haiku), Gemini 2.5 Flash and Pro, Grok 3, Mistral 7B, and several open-source models including Llama 3. The classifier learns the common statistical signatures that cross all models while also learning model-specific patterns that are distinctive.

Pure AI-generated documents are the easiest case for detection. Mixed-authorship documents — where AI generated a draft and a human edited it, or where humans wrote most of it and used AI for specific sections — are more complex.

The sentence-level scoring view is the critical tool for mixed-authorship analysis. When the document-level score is moderate (40–70%), the sentence-level view reveals the structure: a human-written intro, AI-generated body paragraphs, and a human-written conclusion, for example. This pattern is common in AI-assisted writing workflows.

Two common mixed-authorship patterns:

**Draft and edit**: AI generates a full draft, human edits selected sections. Sentence-level scoring shows uneven distribution — some sections remain AI-flagged, others are human-flagged after editing.

**Human-written with AI fills**: Human writes the main structure and arguments, uses AI to generate specific sections, examples, or transitions. Sentence-level scoring shows scattered AI-flagged sentences within predominantly human text.

The document-level score is most useful as a screening tool. The sentence-level view is most useful for understanding the authorship structure and identifying where human review is needed.

Understanding the limitations of AI detection is as important as understanding its capabilities.

**Steganographic watermarks**: Google's SynthID for text (still in limited deployment as of 2026) and proposed cryptographic text watermarking schemes would be undetectable by statistical classifiers. These are embedded in token selection probabilities during generation and are designed specifically to be statistically invisible. If such watermarks are widely deployed in future models, cryptographic detection (not statistical detection) will be required.

**Heavily humanized text**: Text that has been processed through an effective humanizer, or heavily edited by a human, may score below the detection threshold. Statistical detection cannot distinguish well-humanized AI text from genuine human writing.

**Very short text**: Reliable detection requires approximately 150+ words. Shorter inputs do not have enough statistical data for reliable scoring.

**Domain-specific technical writing**: Technical documentation, code comments, and domain-specific jargon-heavy text are harder to classify because the vocabulary constraints are extreme and both human and AI text look similar.

Single-document detection via the free tool covers individual verification needs. Enterprise use cases require batch processing:

**Content agency QA**: Agencies receiving thousands of monthly articles from freelancers use batch detection to screen all submissions automatically before editorial review.

**University honor code enforcement**: Academic integrity offices process hundreds of submissions per semester. Bulk detection with per-document reports integrated into LMS systems is the standard workflow.

**Publisher content screening**: News and media organizations screen freelance pitches and submissions. Integration with editorial workflow tools via API is the enterprise deployment pattern.

**Corporate communications review**: Marketing and PR teams verify that external agencies delivering content have not over-relied on AI generation.

The premium dashboard on this site supports bulk uploads, API access, batch report export, and team management. Contact for enterprise pricing.

AI detection is not a quality signal — it is an authorship signal. These are different things.

High-quality human writing can be hard to detect as human by detectors if the writer uses very structured, formal writing. Low-quality human writing that is rambling and imprecise will score as low-AI-probability because it is statistically irregular.

AI-generated text can be high quality (coherent, well-researched, accurate) while still being detectable because quality and detectability are independent properties.

This matters for how detection results should be used. A high AI score indicates probable AI authorship but says nothing about whether the content is accurate, valuable, or appropriate for the use case. A low AI score confirms probable human authorship but does not indicate quality.

For content quality assessment, use this tool in combination with editorial review rather than as a standalone quality gate.